514 research outputs found
Correlation between the strength of low-temperature T-linear normal-state resistivity and in overdoped electron-doped cuprate superconductors
The recently observed an intimate link between the nature of the strange
metallic normal-state and superconductivity in the overdoped electron-doped
cuprate superconductors is calling for an explanation. Here the intrinsic
correlation between the strength of the low-temperature linear-in-temperature
normal-state resistivity and superconducting transition temperature
in the overdoped electron-doped cuprate superconductors is studied within the
framework of the kinetic-energy-driven superconductivity. On the one hand, the
main ingredient is identified into a electron pairing mechanism involving {\it
the spin excitation}, and then has a dome-like shape doping
dependence with the maximal that occurs at around the optimal
electron doping. On the other hand, in the normal-state above , the
low-temperature linear-in-temperature normal-state resistivity in the overdoped
regime arises from the momentum relaxation due to the electron umklapp
scattering mediated by {\it the same spin excitation}. This {\it same spin
excitation} that governs both the electron umklapp scattering responsible for
the low-temperature linear-in-temperature normal-state resistivity and electron
pairing responsible for superconductivity naturally generates a correlation
between the strength of the low-temperature linear-in-temperature normal-state
resistivity and in the overdoped regime.Comment: 12 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2211.0308
T-linear resistivity in the strange-metal phase of cuprate superconductors due to umklapp scattering from a spin excitation
The strange-metal phase of cuprate superconductors exhibits a linear in
temperature resistivity, however, the origin of this remarkable anomaly is
still not well understood. Here the linear temperature dependence of the
electrical resistivity in the strange-metal phase of cuprate superconductors is
investigated from the underdoped to overdoped regimes. The momentum dependence
of the transport scattering rate arising from the umklapp scattering between
electrons by the exchange of the spin excitation is derived and employed to
calculate the electrical resistivity by making use of the Boltzmann equation.
It is shown that the antinodal umklapp scattering leads to the linear in
temperature resistivity in the low-temperature with the temperature linear
coefficient that decreases with the increase of the doping concentration,
however, the nodal umklapp scattering induces a deviation from the linear in
temperature resistivity in the far lower temperature, and then the quadratic in
temperature resistivity in the far lower temperature is generated by both the
antinodal and nodal umklapp scattering. The theory also shows that the same
spin excitation that acts like a bosonic glue to hold the electron pairs
together also mediates scattering of electrons in the strange-metal phase of
cuprtae superconductors responsible for the linear in temperature resistivity
and the associated electronic structure.Comment: 16 pages, 11 figure
Teaching Text-to-Image Models to Communicate in Dialog
A picture is worth a thousand words, thus, it is crucial for conversational
agents to understand, perceive, and effectively respond with pictures. However,
we find that directly employing conventional image generation techniques is
inadequate for conversational agents to produce image responses effectively. In
this paper, we focus on the innovative dialog-to-image generation task, where
the model synthesizes a high-resolution image aligned with the given dialog
context as a response. To tackle this problem, we design a tailored fine-tuning
approach on the top of state-of-the-art text-to-image generation models to
fully exploit the structural and semantic features in dialog context during
image generation. Concretely, we linearize the dialog context with specific
indicators to maintain the dialog structure, and employ in-domain data to
alleviate the style mismatch between dialog-to-image and conventional image
generation tasks. Empirical results on PhotoChat and MMDialog Corpus show that
our approach brings consistent and remarkable improvement with 3
state-of-the-art pre-trained text-to-image generation backbones.Comment: Work in progres
Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.
BACKGROUND: This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database.
METHODS: Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram.
RESULTS: For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P \u3c 0.001) and 0.854 (95% CI 0.785-0.924, P \u3c 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P \u3c 0.001) and 0.809 (95% CI 0.680-0.939, P \u3c 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P \u3c 0.0001).
CONCLUSIONS: Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis
Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery
With the increasing application of deep learning in various domains, salient
object detection in optical remote sensing images (ORSI-SOD) has attracted
significant attention. However, most existing ORSI-SOD methods predominantly
rely on local information from low-level features to infer salient boundary
cues and supervise them using boundary ground truth, but fail to sufficiently
optimize and protect the local information, and almost all approaches ignore
the potential advantages offered by the last layer of the decoder to maintain
the integrity of saliency maps. To address these issues, we propose a novel
method named boundary-semantic collaborative guidance network (BSCGNet) with
dual-stream feedback mechanism. First, we propose a boundary protection
calibration (BPC) module, which effectively reduces the loss of edge position
information during forward propagation and suppresses noise in low-level
features without relying on boundary ground truth. Second, based on the BPC
module, a dual feature feedback complementary (DFFC) module is proposed, which
aggregates boundary-semantic dual features and provides effective feedback to
coordinate features across different layers, thereby enhancing cross-scale
knowledge communication. Finally, to obtain more complete saliency maps, we
consider the uniqueness of the last layer of the decoder for the first time and
propose the adaptive feedback refinement (AFR) module, which further refines
feature representation and eliminates differences between features through a
unique feedback mechanism. Extensive experiments on three benchmark datasets
demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios
and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent
years. Codes and results have been released on GitHub:
https://github.com/YUHsss/BSCGNet.Comment: Accepted by TGR
Effects of fully open-air [CO2] elevation on leaf photosynthesis and ultrastructure of Isatis indigotica Fort
Traditional Chinese medicine relies heavily on herbs, yet there is no information on how these herb plants would respond to climate change. In order to gain insight into such response, we studied the effect of elevated [CO2] on Isatis indigotica Fort, one of the most popular Chinese herb plants. The changes in leaf photosynthesis,chlorophyll fluorescence, leaf ultrastructure and biomass yield in response to elevated [CO2] (550619 mmol mol–1) were determined at the Free-Air Carbon dioxide Enrichment (FACE) experimental facility in North China. Photosynthetic ability of I. indigotica was improved under elevated [CO2]. Elevated [CO2] increased net photosynthetic rate (PN), water use efficiency (WUE) and maximum rate of electron transport (Jmax) of upper most fully-expended leaves, but not stomatal conductance (gs), transpiration ratio (Tr) and maximum velocity of carboxylation (Vc,max). Elevated [CO2] significantly increased leaf intrinsic efficiency of PSII (Fv’/Fm’) and quantum yield of PSII(WPSII), but decreased leaf non-photochemical quenching (NPQ), and did not affect leaf proportion of open PSII reaction centers (qP) and maximum quantum efficiency of PSII (Fv/Fm). The structural chloroplast membrane, grana layer and stroma thylakoid membranes were intact under elevated [CO2], though more starch grains were accumulated within the chloroplasts than that of under ambient [CO2]. While the yield of I. indigotica was higher due to the improved photosynthesis under elevated [CO2], the content of adenosine, one of the functional ingredients in indigowoad
root was not affected
Introducing Depth into Transformer-based 3D Object Detection
In this paper, we present DAT, a Depth-Aware Transformer framework designed
for camera-based 3D detection. Our model is based on observing two major issues
in existing methods: large depth translation errors and duplicate predictions
along depth axes. To mitigate these issues, we propose two key solutions within
DAT. To address the first issue, we introduce a Depth-Aware Spatial
Cross-Attention (DA-SCA) module that incorporates depth information into
spatial cross-attention when lifting image features to 3D space. To address the
second issue, we introduce an auxiliary learning task called Depth-aware
Negative Suppression loss. First, based on their reference points, we organize
features as a Bird's-Eye-View (BEV) feature map. Then, we sample positive and
negative features along each object ray that connects an object and a camera
and train the model to distinguish between them. The proposed DA-SCA and DNS
methods effectively alleviate these two problems. We show that DAT is a
versatile method that enhances the performance of all three popular models,
BEVFormer, DETR3D, and PETR. Our evaluation on BEVFormer demonstrates that DAT
achieves a significant improvement of +2.8 NDS on nuScenes val under the same
settings. Moreover, when using pre-trained VoVNet-99 as the backbone, DAT
achieves strong results of 60.0 NDS and 51.5 mAP on nuScenes test. Our code
will be soon.Comment: revisio
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